dv-trio: a family-based variant calling pipeline using DeepVariant

Abstract Motivation In 2018, Google published an innovative variant caller, DeepVariant, which converts pileups of sequence reads into images and uses a deep neural network to identify single-nucleotide variants and small insertion/deletions from next-generation sequencing data. This approach outperforms existing state-of-the-art tools. However, DeepVariant was designed to call variants within a single sample. In disease sequencing studies, the ability to examine a family trio (father-mother-affected child) provides greater power for disease mutation discovery. Results To further improve DeepVariant’s variant calling accuracy in family-based sequencing studies, we have developed a family-based variant calling pipeline, dv-trio, which incorporates the trio information from the Mendelian genetic model into variant calling based on DeepVariant. Availability and implementation dv-trio is available via an open source BSD3 license at GitHub (https://github.com/VCCRI/dv-trio/). Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Pisces: An Accurate and Versatile Variant Caller for Somatic and Germline Next-Generation Sequencing Data

10.1101/291641 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tamsen Dunn ◽

Gwenn Berry ◽

Dorothea Emig-Agius ◽

Yu Jiang ◽

Serena Lei ◽

...

Keyword(s):

Next Generation Sequencing ◽

Gene Mutations ◽

Variant Calling ◽

Amplicon Sequencing ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Ras Gene ◽

Generation Sequencing

AbstractMotivationNext-Generation Sequencing (NGS) technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants, suitable for the clinic.ResultsWe have developed Pisces, a rapid, versatile and accurate small variant calling suite designed for somatic and germline amplicon sequencing applications. Pisces accuracy is achieved by four distinct modules, the Pisces Read Stitcher, Pisces Variant Caller, the Pisces Variant Quality Recalibrator, and the Pisces Variant Phaser. Each module incorporates a number of novel algorithmic strategies aimed at reducing noise or increasing the likelihood of detecting a true variant.AvailabilityPisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace™ SequenceHub as part of the TruSeq Amplicon workflow and the Illumina Ampliseq Workflow. Pisces is distributed on Illumina sequencing platforms such as the MiSeq™, and is included in the Praxis™ Extended RAS Panel test which was recently approved by the FDA for the detection of multiple RAS gene [email protected] informationSupplementary data are available online.

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xAtlas: Scalable small variant calling across heterogeneous next-generation sequencing experiments

10.1101/295071 ◽

2018 ◽

Cited By ~ 7

Author(s):

Jesse Farek ◽

Daniel Hughes ◽

Adam Mansfield ◽

Olga Krasheninina ◽

Waleed Nasser ◽

...

Keyword(s):

Next Generation Sequencing ◽

Rapid Development ◽

Variant Calling ◽

Supplementary Information ◽

Data Generation ◽

Next Generation ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Homogeneous Sample ◽

Generation Sequencing

AbstractMotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.ResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30× short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.AvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/[email protected] informationSupplementary data are available at Bioinformatics online.

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Bivartect: accurate and memory-saving breakpoint detection by direct read comparison

Bioinformatics ◽

10.1093/bioinformatics/btaa059 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2725-2730

Author(s):

Keisuke Shimmura ◽

Yuki Kato ◽

Yukio Kawahara

Keyword(s):

Genome Editing ◽

High Throughput Sequencing ◽

Variant Calling ◽

Simulated Data ◽

Supplementary Information ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Node ◽

Single Nucleotide ◽

Target Sites

Abstract Motivation Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. Results Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. Availability and implementation Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. Supplementary information Supplementary data are available at Bioinformatics online.

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UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries

Bioinformatics ◽

10.1093/bioinformatics/btaa053 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2718-2724 ◽

Cited By ~ 5

Author(s):

Vincent Sater ◽

Pierre-Julien Viailly ◽

Thierry Lecroq ◽

Élise Prieur-Gaston ◽

Élodie Bohers ◽

...

Keyword(s):

Tumor Cells ◽

Low Frequency ◽

Variant Calling ◽

Pcr Amplification ◽

Targeted Sequencing ◽

Supplementary Information ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Background Error ◽

Low Frequencies

Abstract Motivation Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. Results We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. Availability and implementation The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

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PhredEM: A Phred-Score-Informed Genotype-Calling Approach for Next-Generation Sequencing Studies

10.1101/046136 ◽

2016 ◽

Author(s):

Peizhou Liao ◽

Glen A. Satten ◽

Yi-juan Hu

Keyword(s):

Logistic Regression ◽

Next Generation Sequencing ◽

Em Algorithm ◽

Error Rates ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Genotype Calling ◽

Sequencing Studies ◽

Generation Sequencing

ABSTRACTA fundamental challenge in analyzing next-generation sequencing data is to determine an individual’s genotype correctly as the accuracy of the inferred genotype is essential to downstream analyses. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too-high threshold may lose data while a too-low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The algorithm, which we call PhredEM, uses the Expectation-Maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. We also develop a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be non-monomorphic require application of the EM algorithm. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project. The results demonstrate that PhredEM is an improved, robust and widely applicable genotype-calling approach for next-generation sequencing studies. The relevant software is freely available.

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Numt identification and removal with RtN!

Bioinformatics ◽

10.1093/bioinformatics/btaa642 ◽

2020 ◽

Vol 36 (20) ◽

pp. 5115-5116 ◽

Cited By ~ 2

Author(s):

August E Woerner ◽

Jennifer Churchill Cihlar ◽

Utpal Smart ◽

Bruce Budowle

Keyword(s):

Mitochondrial Genome ◽

Massively Parallel Sequencing ◽

Sequence Similarity ◽

Variant Calling ◽

Supplementary Information ◽

Mitochondrial Genomes ◽

Sequencing Data ◽

Read Mapping ◽

Genome Data ◽

Mitochondrial Sequences

Abstract Motivation Assays in mitochondrial genomics rely on accurate read mapping and variant calling. However, there are known and unknown nuclear paralogs that have fundamentally different genetic properties than that of the mitochondrial genome. Such paralogs complicate the interpretation of mitochondrial genome data and confound variant calling. Results Remove the Numts! (RtN!) was developed to categorize reads from massively parallel sequencing data not based on the expected properties and sequence identities of paralogous nuclear encoded mitochondrial sequences, but instead using sequence similarity to a large database of publicly available mitochondrial genomes. RtN! removes low-level sequencing noise and mitochondrial paralogs while not impacting variant calling, while competing methods were shown to remove true variants from mitochondrial mixtures. Availability and implementation https://github.com/Ahhgust/RtN Supplementary information Supplementary data are available at Bioinformatics online.

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VikNGS: A C ++ Variant Integration Kit for Next Generation Sequencing Association Analysis

Bioinformatics ◽

10.1093/bioinformatics/btz716 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zeynep Baskurt ◽

Scott Mastromatteo ◽

Jiafen Gong ◽

Richard F Wintle ◽

Stephen W Scherer ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genetic Association ◽

Association Analysis ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Data Sets ◽

Next Generation ◽

Sequencing Data ◽

Combining Data ◽

Generation Sequencing

Abstract Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. If differential genotype uncertainty across studies is not accounted for, combining data sets can produce spurious association results. We developed the Variant Integration Kit for NGS (VikNGS), a fast cross-platform software package, to enable aggregation of several data sets for rare and common variant genetic association analysis of quantitative and binary traits with covariate adjustment. VikNGS also includes a graphical user interface, power simulation functionality and data visualization tools. Availability The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

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ScaleQC: a scalable lossy to lossless solution for NGS data compression

Bioinformatics ◽

10.1093/bioinformatics/btaa543 ◽

2020 ◽

Vol 36 (17) ◽

pp. 4551-4559 ◽

Cited By ~ 1

Author(s):

Rongshan Yu ◽

Wenxian Yang

Keyword(s):

Lossless Compression ◽

Supplementary Information ◽

Next Generation Sequencing Data ◽

Sequencing Data ◽

Source Codes ◽

Compression Performance ◽

Data Rates ◽

Quality Value ◽

Ngs Data ◽

Bit Stream

Abstract Motivation Per-base quality values in Next Generation Sequencing data take a significant portion of storage even after compression. Lossy compression technologies could further reduce the space used by quality values. However, in many applications, lossless compression is still desired. Hence, sequencing data in multiple file formats have to be prepared for different applications. Results We developed a scalable lossy to lossless compression solution for quality values named ScaleQC (Scalable Quality value Compression). ScaleQC is able to provide the so-called bit-stream level scalability that the losslessly compressed bit-stream by ScaleQC can be further truncated to lower data rates without incurring an expensive transcoding operation. Despite its scalability, ScaleQC still achieves comparable compression performance at both lossless and lossy data rates compared to the existing lossless or lossy compressors. Availability and implementation ScaleQC has been integrated with SAMtools as a special quality value encoding mode for CRAM. Its source codes can be obtained from our integrated SAMtools (https://github.com/xmuyulab/samtools) with dependency on integrated HTSlib (https://github.com/xmuyulab/htslib). Supplementary information Supplementary data are available at Bioinformatics online.

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neoepiscope improves neoepitope prediction with multivariant phasing

Bioinformatics ◽

10.1093/bioinformatics/btz653 ◽

2019 ◽

Vol 36 (3) ◽

pp. 713-720 ◽

Cited By ~ 5

Author(s):

Mary A Wood ◽

Austin Nguyen ◽

Adam J Struck ◽

Kyle Ellrott ◽

Abhinav Nellore ◽

...

Keyword(s):

False Negative ◽

Supplementary Information ◽

Supplementary File ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Somatic Variant ◽

Negative Results ◽

Multiple Datasets ◽

False Negative Results

Abstract Motivation The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). Results Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. Availability and implementation neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2–8. Supplementary File 2 contains Supplementary Tables 2–6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. Supplementary information Supplementary data are available at Bioinformatics online.

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ABEMUS: platform-specific and data-informed detection of somatic SNVs in cfDNA

Bioinformatics ◽

10.1093/bioinformatics/btaa016 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2665-2674

Author(s):

Nicola Casiraghi ◽

Francesco Orlando ◽

Yari Ciani ◽

Jenny Xiang ◽

Andrea Sboner ◽

...

Keyword(s):

Cancer Patients ◽

R Package ◽

Circulating Tumor Dna ◽

Supplementary Information ◽

Sequencing Error ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Liquid Biopsies ◽

Non Invasive ◽

Cross Platform

Abstract Motivation The use of liquid biopsies for cancer patients enables the non-invasive tracking of treatment response and tumor dynamics through single or serial blood drawn tests. Next-generation sequencing assays allow for the simultaneous interrogation of extended sets of somatic single-nucleotide variants (SNVs) in circulating cell-free DNA (cfDNA), a mixture of DNA molecules originating both from normal and tumor tissue cells. However, low circulating tumor DNA (ctDNA) fractions together with sequencing background noise and potential tumor heterogeneity challenge the ability to confidently call SNVs. Results We present a computational methodology, called Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic SNVs in cfDNA. We tested the capability of our method to analyze data generated using different platforms with distinct sequencing error properties and we compared ABEMUS performances with other popular SNV callers on both synthetic and real cancer patients sequencing data. Results show that ABEMUS performs better in most of the tested conditions proving its reliability in calling low variant allele frequencies somatic SNVs in low ctDNA levels plasma samples. Availability and implementation ABEMUS is cross-platform and can be installed as R package. The source code is maintained on Github at http://github.com/cibiobcg/abemus, and it is also available at CRAN official R repository. Supplementary information Supplementary data are available at Bioinformatics online.

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